title: Multisite functional connectivity MRI classification of autism: ABIDE results.
abstract: BACKGROUND
  Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange (ABIDE) including resting state fMRI data obtained from 964 subjects and 16 separate international sites.
  
  
  METHODS
  For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million "connections") after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier.
  
  
  RESULTS
  Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampaland fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times.
  
  
  CONCLUSIONS
  Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results. Attempts to use multisite classifiers will likely require improved classification algorithms, longer BOLD imaging times, and standardized acquisition parameters for possible future clinical utility.
authors.affiliation: Interdepartmental Program in Neuroscience, University of Utah Salt Lake City, UT, USA ; Department of Psychiatry, University of Utah Salt Lake City, UT, USA.
authors.forename: Jared A
authors.initials: JA
authors.lastname: Nielsen
authors.forename: Brandon A
authors.initials: BA
authors.lastname: Zielinski
authors.forename: P Thomas
authors.initials: PT
authors.lastname: Fletcher
authors.forename: Andrew L
authors.initials: AL
authors.lastname: Alexander
authors.forename: Nicholas
authors.initials: N
authors.lastname: Lange
authors.forename: Erin D
authors.initials: ED
authors.lastname: Bigler
authors.forename: Janet E
authors.initials: JE
authors.lastname: Lainhart
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Anderson
keywords: ABIDE; autism; classification; fcMRI; functional connectivity